Agent skill · research science · aiskillstore
reasoningbank-with-agentdb
Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.
Why this skill is useful
Adds executable scripts and advanced adaptive learning patterns for self-learning agents that enhance decision-making capabilities.
What it needs
Requires node installed locally. About 5k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
ReasoningBank with AgentDB What This Skill Does Provides ReasoningBank adaptive learning patterns using AgentDB's high-performance backend (150x-12,500x faster). Enables agents to learn from experiences, judge outcomes, distill memories, and improve decision-making over time with 100% backward compatibility. Performance: 150x faster pattern retrieval, 500x faster batch operations, <1ms memory access. Prerequisites Node.js 18+ AgentDB v1.0.7+ (via agentic-flow) Understanding of reinforcement learning concepts (optional) --- Quick Start with CLI Initialize ReasoningBank Database Migrate from Legacy ReasoningBank --- Quick Start with API --- Core ReasoningBank Concepts 1. Trajectory Tracking Track agent execution paths and outcomes: 2. Verdict Judgment Judge whether a trajectory was successful: 3. Memory Distillation Consolidate similar experiences into patterns: --- Integration with Reasoning Agents AgentDB provides 4 reasoning modules that enhance ReasoningBank: 1. PatternMatcher Find similar successful patterns: 2. ContextSynthesizer Generate rich context from multiple memories: 3. MemoryOptimizer Automatically consolidate and prune: 4. ExperienceCurator Filter by quality and relevance: --- Legacy API Compatibility AgentDB maintains 100% backward compatibility with legacy ReasoningBank: --- Performance Characteristics Pattern Search: 150x faster (100µs vs 15ms) Memory Retrieval: <1ms (with cache) Batch Insert: 500x faster (2ms vs 1s for 100 patterns) Trajectory Judgment: <5ms (including retrieval + analysis) Memory Distillation: <50ms (consolidate 100 patterns) --- Advanced Patterns Hierarchical Memory Organize memories by abstraction level: Multi-Domain Learning Transfer learning across domains: --- CLI Operations Database Management Migration --- Troubleshooting Issue: Migration fails Issue: Low confidence scores Issue: Memory growing too large --- Learn More AgentDB Integration: nodemodules/agentic-flow/docs/AGENTDBINTEGRATION.md GitHub: https://github.com/ruvnet …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/reasoningbank-with-agentdb--919c55